Page 107 - Read Online
P. 107
Page 22 of 23 Yu et al. Carbon Footprints 2025, 4, 17 https://dx.doi.org/10.20517/cf.2025.12
49. Zhao, P.; Li, Z.; Xiao, Z.; Jiang, S.; He, Z.; Zhang, M. Spatiotemporal characteristics and driving factors of CO emissions from road
2
freight transportation. Transp. Res. Part. D. Transp. Environ. 2023, 125, 103983. DOI
50. Wen, Y.; Zhang, S.; Zhang, J.; et al. Mapping dynamic road emissions for a megacity by using open-access traffic congestion index
data. Appl. Energy. 2020, 260, 114357. DOI
51. Li, M.; Tang, Y.; Wu, K.; Cheng, H. Autonomous vehicle pollution monitoring: an innovative solution for policy and environmental
management. Transp. Res. D. Transp. Environ. 2025, 139, 104542. DOI
52. Li, Z.; Song, G.; Yu, X.; Yu, L.; He, W. Developing operating mode distributions from sparse trajectories for emission estimation.
Transp. Res. Rec. 2019, 2673, 137-48. DOI
53. Weng, J.; Liang, Q.; Qiao, G.; Chen, Z.; Rong, J. Taxi fuel consumption and emissions estimation model based on the reconstruction
of driving trajectory. Adv. Mech. Eng. 2017, 9, 168781401770870. DOI
54. Ma, W.; Liu, Y.; Alimo, P. K.; Wang, L. Vehicle carbon emission estimation for urban traffic based on sparse trajectory data. Int. J.
Transp. Sci. Technol. 2024, 16, 222-33. DOI
55. Shang, W.; Zhang, M.; Wu, G.; Yang, L.; Fang, S.; Ochieng, W. Estimation of traffic energy consumption based on macro-micro
modelling with sparse data from connected and automated vehicles. Appl. Energy. 2023, 351, 121916. DOI
56. Smit, R.; Awadallah, M.; Bagheri, S.; Surawski, N. Real-world emission factors for SUVs using on-board emission testing and geo-
computation. Transp. Res. D. Transp. Environ. 2022, 107, 103286. DOI
57. Yaacob, N. F. F.; Mat, Y. M. R.; Abdul, M. K. N.; Ahmad, B. N. E. A review of the measurement method, analysis and
implementation policy of carbon dioxide emission from transportation. Sustainability 2020, 12, 5873. DOI
58. Shan, X. N.; Chen, X. H. Review of research on the integration of traffic simulation models and microscopic vehicle emission
models. J. Transp. Eng. Inf. 2021, 19, 11-24. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=cjfd2021&filename=
jtgc202102002&dbcode=cjfq (accessed 2025-06-09). (in Chinese)
59. Lejri, D.; Can, A.; Schiper, N.; Leclercq, L. Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant
emissions at the urban scale. Transp. Res. D. Transp. Environ. 2018, 63, 588-603. DOI
60. Gräbe, R. J.; Joubert, J. W. Are we getting vehicle emissions estimation right? Transp. Res. D. Transp. Environ. 2022, 112, 103477.
DOI
61. Chen, Y.; Wen, C.; Jiang, C.; Jiang, X. Global sensitivity analysis of VISSIM parameters for project-level traffic emissions: a case
study at a signalized intersection. Environ. Technol. 2022, 43, 3801-20. DOI
62. Shan, X. N.; Liu, H. B.; Zhang, X. L.; Chen, X. H.; Ye, J. H. Estimation method of light-duty vehicle emission factors based on the
localization of MOVES model. J. Tongji. Univ. (Nat. Sci). 2021, 49, 1135-143+1201. https://kns.cnki.net/KCMS/detail/detail.
aspx?dbname=cjfdauto&filename=tjdz202108010&dbcode=cjfq (accessed 2025-06-09). (in Chinese)
63. Zhang, X.; Zhang, Z.; Liu, Y.; Xu, Z.; Qu, X. A review of machine learning approaches for electric vehicle energy consumption
modelling in urban transportation. Renew. Energy. 2024, 234, 121243. DOI
64. Wu, K.; Ding, J.; Lin, J.; et al. Big-data empowered traffic signal control could reduce urban carbon emission. Nat. Commun. 2025,
16, 2013. DOI PubMed PMC
65. Chandrashekar, C.; Chatterjee, P.; Pawar, D. S. Modeling real-world diesel car tailpipe emissions using regression-based approaches.
Transp. Res. D. Transp. Environ. 2024, 128, 104092. DOI
66. Jia, Z.; Yin, J.; Cao, Z.; et al. Large-scale deployment of intelligent transportation to help achieve low-carbon and clean sustainable
transportation. Sci. Total. Environ. 2024, 949, 174724. DOI
67. Shin, S.; Lee, Y.; Lee, Y.; et al. Designing a steady-state experimental dataset for predicting transient NO emissions of diesel engines
x
via deep learning. Expert. Syst. Appl. 2022, 198, 116919. DOI
68. Fei, X.; Long, F.; Li, F.; Ling, Q. Multi-component fusion temporal networks to predict vehicle exhaust based on remote monitoring
data. IEEE. Access. 2021, 9, 42358-69. DOI
69. Mądziel, M. Future cities carbon emission models: hybrid vehicle emission modelling for low-emission zones. Energies 2023, 16,
6928. DOI
70. Shepelev, V.; Glushkov, A.; Slobodin, I.; Cherkassov, Y. Measuring and modelling the concentration of vehicle-related PM2.5 and
PM10 emissions based on neural networks. Mathematics 2023, 11, 1144. DOI
71. Sun, Y.; Hu, Y.; Zhang, H.; Wang, F.; Chen, H. A parallel supervision system for vehicle CO emissions based on OBD-independent
2
information. IEEE. Trans. Intell. Veh. 2023, 8, 2077-87. DOI
72. Zhang, Q.; Li, F.; Long, F.; Ling, Q. Vehicle emission forecasting based on wavelet transform and long short-term memory network.
IEEE. Access. 2018, 6, 56984-94. DOI
73. Howlader, A. M.; Patel, D.; Gammariello, R. Data-driven approach for instantaneous vehicle emission predicting using integrated
deep neural network. Transp. Res. D. Transp. Environ. 2023, 116, 103654. DOI
74. Sun, Z.; Wang, C.; Ye, Z.; Bi, H. Long short-term memory network-based emission models for conventional and new energy buses.
Int. J. Sustain. Transp. 2021, 15, 229-38. DOI
75. Li, S.; Tong, Z.; Haroon, M. Estimation of transport CO emissions using machine learning algorithm. Transp. Res. D. Transp.
2
Environ. 2024, 133, 104276. DOI
76. Singh, M.; Dubey, R. K. Deep learning model based CO emissions prediction using vehicle telematics sensors data. IEEE. Trans.
2
Intell. Veh. 2023, 8, 768-77. DOI
77. Song, G.; Zhou, X.; Yu, L. Delay correction model for estimating bus emissions at signalized intersections based on vehicle specific

